Traders, investment bankers, researchers, insurance underwriters, and other financial professionals (referred to as a “financial decision-maker” or “trader) rely on information contained in financial market databases to inform their decisions. Markets on which financial instruments are traded, such as the New York Stock Exchange, are extraordinarily volatile and dynamic, so traders need real-time data about the state of the market and historical data about previous market environments to make wise, informed investment decisions. Moreover, certain markets may facilitate the simultaneous buying and selling of millions of financial instruments (i.e. on a given market, thousands of securities issued by thousands of firms may be traded at substantially the same time), may be capable of changing drastically in a few seconds' time, or may exhibit characteristics that could be better understood through research of decades of market history. Traders are therefore unable to remain abreast of the developments in each relevant market based on actual personal perception alone.
To enable immediate, meaningful, and manageable access to information about such markets, many firms provide and maintain databases with detailed and comprehensive historical financial data, continuously updated with real-time financial data. Firms such as Commodity Systems, Inc., Hemscott, Morningstar, Cannex, Hoovers, Thomson Financial, Standard & Poor's, Reuters, Bloomberg, Interactive Data, and Gradient Analytics provide databases accessible to end users over the Internet or other similar networks. For example, data about firms whose securities are traded on the New York Stock Exchange may be contained in a database maintained by one of the above firms. Further, information about each transaction of a security or other financial instrument may also be contained in such a database. Traders engaging in transactions of instruments traded on the relevant market (e.g. the New York Stock Exchange) are given client-level access to a database associated with the market. This access provides users with client-level privileges, and enables traders to perform basic searches, such as keyword or rule-based searches. Traders perform what they perceive to be necessary or relevant searches and analyze the results to determine whether or not to buy or sell a security or other financial instrument.
The searching functionality enabled by the databases and available to traders is typically rudimentary, and usually does not provide traders with tools to usefully analyze data or to meaningfully contribute to the traders' decision-making process. However, client-level access to such databases enables the development and implementation of client-side software applications. Such applications are capable of performing the same keyword and rule-based searches, but provide the added benefits of enabling multiple searches to be performed in quick succession and assimilating and organizing the results of such searches into more meaningful formats for near real-time analysis by the trader.
Searches that are currently enabled by available client-side software applications are limited by their static nature. A client-side software application may store sets of search criteria and repeatedly run the same search without user intervention (i.e. once a day for a month, or once a week for a year). However, a trader must initially indicate with specificity the criteria defining the automated search. Thus, each search requires the trader to synthesize the universe of the trader's knowledge to generate the database queries. Moreover, currently available search tools do not enable the criteria to be automatically updated or dynamically improved based on the trader's propensities, the results of previous searches, or the eventual success of positions taken based the search results.
Current software solutions are also limited in that they require the trader to define with specificity the logic necessary to usefully summarize, present, and analyze data retrieved from financial databases. Current software applications may manipulate and organize data to display it such a way as to enable a trader to quickly analyze the data, but the trader must still specify the parameters enabling the software to so organize the data. Thus, if a trader is searching for a particular pattern in a time series data set, the trader must search each potential data set and manually analyze whether a similar pattern can be found. Current search capabilities and client-side software applications do not organize and summarize data based on features determined by information processing, system learning, or domain knowledge contained in a local knowledge depository.
Finally, currently available client-side software is not capable of building a knowledge base of a particular user's prior searches, trades, and other decisions to enable decision support. Current client-side software is limited to statically storing previous search criteria, data presentation parameters, and the resulting trades. The software is not capable of intelligently determining relationships between, for example, a change in search criteria that results in the trader making a particular trade based on the change in criteria and the success of the resulting trade. Though current solutions enable searches to be replicated and repeated, these solutions cannot analyze any relationships between the searches and the actions resulting from the searches to aid the trader in making similar decisions in the future.